Defining “Approachability” of Nursery Pigs can Result in Differing Conclusions
Bibliographic record
Abstract
The objective of this experiment was to compare two approachability definitions of nursery pigs to a human observer in their home pen using a digital image. A total of 79 pens in two rooms (40 in room 1 and 39 in room 2) were used. A total of 1,817, ~6 wk old mixed sexed nursery pigs, weighing ~25.4 kg were used. Two definitions for pigs reacting to a human in their home pen were compared. Determining the approachability of pigs followed procedures used by Fangman et al., (2010). The experimental unit was the pen of pigs. Data used to evaluate nursery pig behaviors failed to meet the assumption of normally distributed data. These data were analyzed by using the PROC GLIMMIX procedure of SAS. A P-value of ≤ 0.05 was considered to be significant for all measures. There were differences in the number of pigs classified as Approaching, Look, or Not based on the definitions. There were more pigs classified as Approaching and fewer pigs classified as Look and Not when using the standard definition for WTA compared to the alternative definition. Therefore in conclusion, the definition for “approachability” becomes important, if it were to be used for on-farm welfare assessment or auditing. Additionally, using approachability without Look and Not would not provide the external observer complete information on the pigs comfort level. In particular, when pigs are recorded as “Not” it is vital that further classification of behaviors and postures are recorded. For example, are pigs feeding, drinking, socializing or resting. These entire main and sub behavioral classifications can then result in an accurate assessment of pig behavior when presented with a human in their home pen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".